# Streetscape Analysis with Generative AI (SAGAI): Vision-Language Assessment and Mapping of Urban Scenes

arXiv: https://arxiv.org/abs/2504.16538 · HTML: https://mlsanity.com/paper/2504.16538/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents](https://mlsanity.com/paper/2311.05437/) (2023)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials](https://mlsanity.com/paper/2201.02260/) (2022)

## Built on by (most cited first, 6 of 6)

- [StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery](https://mlsanity.com/paper/2506.14670/) (2025)
- [Culturally uneven urban perception in large language models](https://mlsanity.com/paper/2604.20048/) (2026)
- [UVLM: A Universal Vision-Language Model Loader for Reproducible Multimodal Benchmarking](https://mlsanity.com/paper/2603.13893/) (2026)
- [UrbanVGGT: Scalable Sidewalk Width Estimation from Street View Images](https://mlsanity.com/paper/2603.22531/) (2026)
- [From Street View Imagery to Street Quality Indicators: Vision Language Inference for the Suburban 15-minute City](https://mlsanity.com/paper/2608.20026/) (2026)
- [Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?](https://mlsanity.com/paper/2609.17882/) (2026)

## Abstract (arXiv)

Streetscapes are an essential component of urban space. Their assessment is presently either limited to morphometric properties of their mass skeleton or requires labor-intensive qualitative evaluations of visually perceived qualities. This paper introduces SAGAI: Streetscape Analysis with Generative Artificial Intelligence, a modular workflow for scoring street-level urban scenes using open-access data and vision-language models. SAGAI integrates OpenStreetMap geometries, Google Street View imagery, and a lightweight version of the LLaVA model to generate structured spatial indicators from images via customizable natural language prompts. The pipeline includes an automated mapping module that aggregates visual scores at both the point and street levels, enabling direct cartographic interpretation. It operates without task-specific training or proprietary software dependencies, supporting scalable and interpretable analysis of urban environments. Two exploratory case studies in Nice and Vienna illustrate SAGAI's capacity to produce geospatial outputs from vision-language inference. The initial results show strong performance for binary urban-rural scene classification, moderate precision in commercial feature detection, and lower estimates, but still informative, of sidewalk width. Fully deployable by any user, SAGAI can be easily adapted to a wide range of urban research themes, such as walkability, safety, or urban design, through prompt modification alone.